Stage division and fault detection method based on correlation analysis

A technology of correlation analysis and fault detection, applied in electrical testing/monitoring, testing/monitoring control systems, instruments, etc., can solve problems such as the reduction of the accuracy of the representative model of the stage, the inability of the clustering algorithm to consider the sequence sequence of stages, and the intersection of time sequences.

Active Publication Date: 2018-10-16
HUZHOU TEACHERS COLLEGE
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Problems solved by technology

The basic idea of ​​the cluster-based stage identification method is that the change of the industrial process relationship is related to the switching of multiple stages. This type of method can effectively capture the characteristics of the dynamic industrial process without filling unknown and missing data, but this type of method still exists. Some deficiencies, for example, the multi-stage division of the clustering algorithm cannot consider the order and timing of

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  • Stage division and fault detection method based on correlation analysis
  • Stage division and fault detection method based on correlation analysis
  • Stage division and fault detection method based on correlation analysis

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[0042] In the present invention, partial least squares (PLS) is an important method in multivariate statistical analysis, which mainly focuses on finding the relationship between the multidimensional matrix X and Y, and extracting the optimal low-dimensional feature interpretation direction. is built in the sense of predictive power from the input space to the output space. Multidirectional Partial Least Squares (MPLS) is an extension of PLS, by combining multiple batches of historical industrial process data matrix X(I×J×K) and quality data matrix Y(I×M×K) with variables Or batch-wise expanded into two-dimensional matrices X(IK×J) and Y(IK×M), and extract low-dimensional latent features. The above expansion method not only preserves the nonlinear time-varying trajectory between variables, but also preserves the average trajectory between batches. Among them, the monitoring method based on the variable expansion method neither needs forecast data nor requires batches of equal...

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Abstract

The invention relates to a stage division and fault detection method based on correlation analysis. The method uses the change degree of information in the time slice matrix of a stable stage and a transition stage of an industrial process to identify multiple stages of the reaction duration of the whole industrial process. The method comprises the steps of arranging and standardizing historical batch data in a variable expansion mode, unfolding according to a batch mode, the unfolded time slice matrix comprising time-varying characteristics of an industrial process, and carrying out time sequence stage division according to distribution characteristics of evaluation values. After phase division is carried out, a model is established for each duration stage to monitor quality-related faults, carry out residual information subspace extraction on the industrial process variables, and monitor the abnormity irrelevant to quality in the industrial process. The method is applied to an industrial penicillin fermentation industrial process, which shows that the method has better monitoring performance and forecast capability.

Description

technical field [0001] The invention belongs to the technical field of automatic control, and relates to a phase division and fault detection method based on correlation analysis. Background technique [0002] The performance monitoring of industrial processes generally starts from historical production data, establishes corresponding performance monitoring and fault detection models through statistical information processing methods, and uses this model to monitor the industrial process of product production, discover and eliminate abnormalities in industrial processes in time conditions, so that the industrial process of production can run efficiently, safely and stably. Traditional analytical model-based industrial process monitoring requires accurate mathematics and production experience, which limits its practical application. Multi-way Principle Component Analysis (MPCA) and multi-way partial least squares (Multi-way partial Least Squares, MPLS) are commonly used stat...

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Application Information

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IPC IPC(8): G05B23/02
CPCG05B23/024
Inventor 王培良叶晓丰杨泽宇
Owner HUZHOU TEACHERS COLLEGE
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